| Title | Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures |
|---|---|
| ID_Doc | 75375 |
| Authors | Iwendi, C; Rehman, SU; Javed, AR; Khan, S; Srivastava, G |
| Title | Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures |
| Year | 2021 |
| Published | Acm Transactions On Internet Technology, 21, 3 |
| Abstract | In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business' operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency. |
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